The effects on public health of climate change adaptation responses: a systematic review of evidence from low- and middle-income countries
Bibliographic record
Abstract
Abstract Climate change adaptation responses are being developed and delivered in many parts of the world in the absence of detailed knowledge of their effects on public health. Here we present the results of a systematic review of peer-reviewed literature reporting the effects on health of climate change adaptation responses in low- and middle-income countries (LMICs). The review used the ‘Global Adaptation Mapping Initiative’ database (comprising 1682 publications related to climate change adaptation responses) that was constructed through systematic literature searches in Scopus, Web of Science and Google Scholar (2013–2020). For this study, further screening was performed to identify studies from LMICs reporting the effects on human health of climate change adaptation responses. Studies were categorised by study design and data were extracted on geographic region, population under investigation, type of adaptation response and reported health effects. The review identified 99 studies (1117 reported outcomes), reporting evidence from 66 LMICs. Only two studies were ex ante formal evaluations of climate change adaptation responses. Papers reported adaptation responses related to flooding, rainfall, drought and extreme heat, predominantly through behaviour change, and infrastructural and technological improvements. Reported (direct and intermediate) health outcomes included reduction in infectious disease incidence, improved access to water/sanitation and improved food security. All-cause mortality was rarely reported, and no papers were identified reporting on maternal and child health. Reported maladaptations were predominantly related to widening of inequalities and unforeseen co-harms. Reporting and publication-bias seems likely with only 3.5% of all 1117 health outcomes reported to be negative. Our review identified some evidence that climate change adaptation responses may have benefits for human health but the overall paucity of evidence is concerning and represents a major missed opportunity for learning. There is an urgent need for greater focus on the funding, design, evaluation and standardised reporting of the effects on health of climate change adaptation responses to enable evidence-based policy action.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".